Overview

Dataset statistics

Number of variables10
Number of observations53908
Missing cells0
Missing cells (%)0.0%
Duplicate rows142
Duplicate rows (%)0.3%
Total size in memory5.9 MiB
Average record size in memory115.2 B

Variable types

Numeric9
Categorical1

Alerts

Dataset has 142 (0.3%) duplicate rowsDuplicates
carat is highly overall correlated with price and 3 other fieldsHigh correlation
price is highly overall correlated with carat and 3 other fieldsHigh correlation
x is highly overall correlated with carat and 3 other fieldsHigh correlation
y is highly overall correlated with carat and 3 other fieldsHigh correlation
z is highly overall correlated with carat and 3 other fieldsHigh correlation
color has 6774 (12.6%) zerosZeros
clarity has 732 (1.4%) zerosZeros

Reproduction

Analysis started2023-01-25 07:57:37.781749
Analysis finished2023-01-25 07:57:47.080735
Duration9.3 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

carat
Real number (ℝ)

Distinct268
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.79726942
Minimum0.2
Maximum3.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:47.151423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.3
Q10.4
median0.7
Q31.04
95-th percentile1.7
Maximum3.67
Range3.47
Interquartile range (IQR)0.64

Descriptive statistics

Standard deviation0.47235698
Coefficient of variation (CV)0.59246846
Kurtosis0.9593992
Mean0.79726942
Median Absolute Deviation (MAD)0.32
Skewness1.0829479
Sum42979.2
Variance0.22312112
MonotonicityNot monotonic
2023-01-25T13:27:47.254158image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 2604
 
4.8%
0.31 2249
 
4.2%
1.01 2240
 
4.2%
0.7 1981
 
3.7%
0.32 1840
 
3.4%
1 1556
 
2.9%
0.9 1485
 
2.8%
0.41 1382
 
2.6%
0.4 1299
 
2.4%
0.71 1292
 
2.4%
Other values (258) 35980
66.7%
ValueCountFrequency (%)
0.2 12
 
< 0.1%
0.21 9
 
< 0.1%
0.22 5
 
< 0.1%
0.23 293
0.5%
0.24 254
0.5%
0.25 212
0.4%
0.26 253
0.5%
0.27 233
0.4%
0.28 198
0.4%
0.29 130
0.2%
ValueCountFrequency (%)
3.67 1
< 0.1%
3.65 1
< 0.1%
3.51 1
< 0.1%
3.5 1
< 0.1%
3.4 1
< 0.1%
3.24 1
< 0.1%
3.22 1
< 0.1%
3.11 1
< 0.1%
3.05 1
< 0.1%
3.04 2
< 0.1%

cut
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size842.3 KiB
2
21544 
3
13777 
4
12079 
1
4902 
0
 
1606

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters53908
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row1
4th row3
5th row1

Common Values

ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

Length

2023-01-25T13:27:47.340791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-25T13:27:47.415540image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

Most occurring characters

ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 21544
40.0%
3 13777
25.6%
4 12079
22.4%
1 4902
 
9.1%
0 1606
 
3.0%

color
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5936967
Minimum0
Maximum6
Zeros6774
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size631.7 KiB
2023-01-25T13:27:47.488295image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7011154
Coefficient of variation (CV)0.65586522
Kurtosis-0.86672627
Mean2.5936967
Median Absolute Deviation (MAD)1
Skewness0.18970512
Sum139821
Variance2.8937937
MonotonicityNot monotonic
2023-01-25T13:27:47.561050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
3 11284
20.9%
1 9795
18.2%
2 9537
17.7%
4 8295
15.4%
0 6774
12.6%
5 5418
10.1%
6 2805
 
5.2%
ValueCountFrequency (%)
0 6774
12.6%
1 9795
18.2%
2 9537
17.7%
3 11284
20.9%
4 8295
15.4%
5 5418
10.1%
6 2805
 
5.2%
ValueCountFrequency (%)
6 2805
 
5.2%
5 5418
10.1%
4 8295
15.4%
3 11284
20.9%
2 9537
17.7%
1 9795
18.2%
0 6774
12.6%

clarity
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8359427
Minimum0
Maximum7
Zeros732
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size631.7 KiB
2023-01-25T13:27:47.630894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median4
Q35
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7242259
Coefficient of variation (CV)0.44949208
Kurtosis-0.82231017
Mean3.8359427
Median Absolute Deviation (MAD)1
Skewness0.17564092
Sum206788
Variance2.9729549
MonotonicityNot monotonic
2023-01-25T13:27:47.696596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 13061
24.2%
5 12254
22.7%
3 9184
17.0%
4 8167
15.1%
7 5066
 
9.4%
6 3654
 
6.8%
1 1790
 
3.3%
0 732
 
1.4%
ValueCountFrequency (%)
0 732
 
1.4%
1 1790
 
3.3%
2 13061
24.2%
3 9184
17.0%
4 8167
15.1%
5 12254
22.7%
6 3654
 
6.8%
7 5066
 
9.4%
ValueCountFrequency (%)
7 5066
 
9.4%
6 3654
 
6.8%
5 12254
22.7%
4 8167
15.1%
3 9184
17.0%
2 13061
24.2%
1 1790
 
3.3%
0 732
 
1.4%

depth
Real number (ℝ)

Distinct184
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.749373
Minimum43
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:47.782320image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile59.3
Q161
median61.8
Q362.5
95-th percentile63.8
Maximum79
Range36
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.4321416
Coefficient of variation (CV)0.023192812
Kurtosis5.7500791
Mean61.749373
Median Absolute Deviation (MAD)0.7
Skewness-0.082274287
Sum3328785.2
Variance2.0510295
MonotonicityNot monotonic
2023-01-25T13:27:47.876995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 2239
 
4.2%
61.9 2162
 
4.0%
61.8 2075
 
3.8%
62.2 2038
 
3.8%
62.1 2019
 
3.7%
61.6 1955
 
3.6%
62.3 1940
 
3.6%
61.7 1904
 
3.5%
62.4 1792
 
3.3%
61.5 1719
 
3.2%
Other values (174) 34065
63.2%
ValueCountFrequency (%)
43 2
< 0.1%
44 1
< 0.1%
50.8 1
< 0.1%
51 1
< 0.1%
52.2 1
< 0.1%
52.3 1
< 0.1%
52.7 1
< 0.1%
53 1
< 0.1%
53.1 1
< 0.1%
53.2 2
< 0.1%
ValueCountFrequency (%)
79 2
< 0.1%
78.2 1
< 0.1%
73.6 1
< 0.1%
72.9 1
< 0.1%
72.2 1
< 0.1%
71.8 1
< 0.1%
71.6 2
< 0.1%
71.3 1
< 0.1%
71.2 1
< 0.1%
71 1
< 0.1%

table
Real number (ℝ)

Distinct127
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.456775
Minimum43
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:47.973715image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile54
Q156
median57
Q359
95-th percentile61
Maximum95
Range52
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2339938
Coefficient of variation (CV)0.038881295
Kurtosis2.8031094
Mean57.456775
Median Absolute Deviation (MAD)1
Skewness0.7969554
Sum3097379.8
Variance4.9907284
MonotonicityNot monotonic
2023-01-25T13:27:48.071352image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 9878
18.3%
57 9722
18.0%
58 8364
15.5%
59 6564
12.2%
55 6267
11.6%
60 4239
7.9%
54 2592
 
4.8%
61 2278
 
4.2%
62 1272
 
2.4%
63 588
 
1.1%
Other values (117) 2144
 
4.0%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 1
 
< 0.1%
49 2
 
< 0.1%
50 2
 
< 0.1%
50.1 1
 
< 0.1%
51 9
 
< 0.1%
51.6 1
 
< 0.1%
52 56
0.1%
52.4 1
 
< 0.1%
52.8 2
 
< 0.1%
ValueCountFrequency (%)
95 1
 
< 0.1%
79 1
 
< 0.1%
76 1
 
< 0.1%
73 4
 
< 0.1%
71 1
 
< 0.1%
70 9
 
< 0.1%
69 9
 
< 0.1%
68 21
 
< 0.1%
67 41
0.1%
66 91
0.2%

price
Real number (ℝ)

Distinct11593
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3929.2491
Minimum326
Maximum18823
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:48.159122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum326
5-th percentile544
Q1949
median2400.5
Q35321
95-th percentile13090.6
Maximum18823
Range18497
Interquartile range (IQR)4372

Descriptive statistics

Standard deviation3985.0936
Coefficient of variation (CV)1.0142125
Kurtosis2.1807202
Mean3929.2491
Median Absolute Deviation (MAD)1669.5
Skewness1.6186343
Sum2.1181796 × 108
Variance15880971
MonotonicityNot monotonic
2023-01-25T13:27:48.239856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
605 132
 
0.2%
802 127
 
0.2%
625 126
 
0.2%
828 125
 
0.2%
776 124
 
0.2%
789 121
 
0.2%
698 121
 
0.2%
544 120
 
0.2%
666 114
 
0.2%
552 113
 
0.2%
Other values (11583) 52685
97.7%
ValueCountFrequency (%)
326 2
< 0.1%
327 1
< 0.1%
334 1
< 0.1%
335 1
< 0.1%
336 2
< 0.1%
337 2
< 0.1%
338 1
< 0.1%
339 1
< 0.1%
340 1
< 0.1%
342 1
< 0.1%
ValueCountFrequency (%)
18823 1
< 0.1%
18818 1
< 0.1%
18806 1
< 0.1%
18804 1
< 0.1%
18803 1
< 0.1%
18797 1
< 0.1%
18795 2
< 0.1%
18791 2
< 0.1%
18787 1
< 0.1%
18784 1
< 0.1%

x
Real number (ℝ)

Distinct547
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7310318
Minimum3.73
Maximum9.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:48.327560image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.73
5-th percentile4.29
Q14.71
median5.7
Q36.54
95-th percentile7.66
Maximum9.86
Range6.13
Interquartile range (IQR)1.83

Descriptive statistics

Standard deviation1.1184522
Coefficient of variation (CV)0.19515722
Kurtosis-0.72348831
Mean5.7310318
Median Absolute Deviation (MAD)0.92
Skewness0.39365217
Sum308948.46
Variance1.2509354
MonotonicityNot monotonic
2023-01-25T13:27:48.406265image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 448
 
0.8%
4.34 437
 
0.8%
4.33 429
 
0.8%
4.38 428
 
0.8%
4.32 425
 
0.8%
4.35 407
 
0.8%
4.39 388
 
0.7%
4.31 387
 
0.7%
4.36 386
 
0.7%
4.4 373
 
0.7%
Other values (537) 49800
92.4%
ValueCountFrequency (%)
3.73 2
 
< 0.1%
3.74 1
 
< 0.1%
3.76 1
 
< 0.1%
3.77 1
 
< 0.1%
3.79 2
 
< 0.1%
3.81 3
< 0.1%
3.82 2
 
< 0.1%
3.83 3
< 0.1%
3.84 4
< 0.1%
3.85 6
< 0.1%
ValueCountFrequency (%)
9.86 1
 
< 0.1%
9.66 1
 
< 0.1%
9.65 1
 
< 0.1%
9.54 1
 
< 0.1%
9.53 1
 
< 0.1%
9.51 1
 
< 0.1%
9.49 1
 
< 0.1%
9.44 3
< 0.1%
9.42 2
< 0.1%
9.41 1
 
< 0.1%

y
Real number (ℝ)

Distinct543
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.732866
Minimum3.68
Maximum9.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:48.491017image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum3.68
5-th percentile4.3
Q14.72
median5.71
Q36.54
95-th percentile7.64
Maximum9.81
Range6.13
Interquartile range (IQR)1.82

Descriptive statistics

Standard deviation1.1103599
Coefficient of variation (CV)0.19368322
Kurtosis-0.73491855
Mean5.732866
Median Absolute Deviation (MAD)0.92
Skewness0.38838966
Sum309047.34
Variance1.2328992
MonotonicityNot monotonic
2023-01-25T13:27:48.570754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.34 437
 
0.8%
4.37 435
 
0.8%
4.35 425
 
0.8%
4.33 421
 
0.8%
4.32 414
 
0.8%
4.39 407
 
0.8%
4.38 406
 
0.8%
4.4 387
 
0.7%
4.31 386
 
0.7%
4.41 384
 
0.7%
Other values (533) 49806
92.4%
ValueCountFrequency (%)
3.68 1
 
< 0.1%
3.71 2
 
< 0.1%
3.72 1
 
< 0.1%
3.73 1
 
< 0.1%
3.75 1
 
< 0.1%
3.77 2
 
< 0.1%
3.78 5
< 0.1%
3.8 1
 
< 0.1%
3.81 1
 
< 0.1%
3.82 1
 
< 0.1%
ValueCountFrequency (%)
9.81 1
< 0.1%
9.63 1
< 0.1%
9.59 1
< 0.1%
9.48 1
< 0.1%
9.46 1
< 0.1%
9.42 1
< 0.1%
9.4 1
< 0.1%
9.38 2
< 0.1%
9.37 1
< 0.1%
9.34 1
< 0.1%

z
Real number (ℝ)

Distinct363
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5392049
Minimum2.06
Maximum6.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size842.3 KiB
2023-01-25T13:27:48.965512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.06
5-th percentile2.65
Q12.91
median3.53
Q34.04
95-th percentile4.73
Maximum6.38
Range4.32
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.6907805
Coefficient of variation (CV)0.19517957
Kurtosis-0.72451483
Mean3.5392049
Median Absolute Deviation (MAD)0.57
Skewness0.38926874
Sum190791.46
Variance0.4771777
MonotonicityNot monotonic
2023-01-25T13:27:49.047238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.7 767
 
1.4%
2.69 748
 
1.4%
2.71 738
 
1.4%
2.68 730
 
1.4%
2.72 697
 
1.3%
2.67 649
 
1.2%
2.73 612
 
1.1%
2.66 555
 
1.0%
2.74 548
 
1.0%
4.02 538
 
1.0%
Other values (353) 47326
87.8%
ValueCountFrequency (%)
2.06 1
 
< 0.1%
2.24 1
 
< 0.1%
2.25 1
 
< 0.1%
2.26 1
 
< 0.1%
2.27 1
 
< 0.1%
2.28 1
 
< 0.1%
2.29 1
 
< 0.1%
2.3 2
 
< 0.1%
2.31 6
< 0.1%
2.32 3
< 0.1%
ValueCountFrequency (%)
6.38 1
< 0.1%
6.27 1
< 0.1%
6.16 1
< 0.1%
6.13 1
< 0.1%
6.03 2
< 0.1%
5.98 1
< 0.1%
5.97 1
< 0.1%
5.92 1
< 0.1%
5.91 1
< 0.1%
5.9 2
< 0.1%

Interactions

2023-01-25T13:27:45.746690image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:38.607432image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.622553image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.520473image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.658557image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.454364image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.218344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.014318image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.921156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.849421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:38.783099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.749107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.889820image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.748274image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.541069image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.309074image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.103128image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.007939image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.930119image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:38.888947image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.833642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.039319image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.837956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.624788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.395793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.187843image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.093651image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.015787image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:38.984688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.923463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.132083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.929655image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.717401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.485485image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.275550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.180361image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.146963image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.090345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.013865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.224149image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.020346image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.806439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.576107image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.362269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.267996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.283821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.229966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.095269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.310649image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.104067image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.887414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.659828image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.582522image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.348722image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.378196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.330608image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.184892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.401344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.194770image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.974133image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.753587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.670238image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.437960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.498793image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.415327image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.271602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.488138image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.282395image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.057917image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.840789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.754871image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.521524image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:46.693547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:39.501959image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:40.407146image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:41.576794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:42.370566image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.140640image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:43.929127image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:44.839330image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2023-01-25T13:27:45.613144image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2023-01-25T13:27:49.121989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
caratcolorclaritydepthtablepricexyzcut
carat1.0000.249-0.2160.0300.1950.9630.9970.9960.9950.113
color0.2491.000-0.0230.0490.0280.1500.2450.2450.2510.036
clarity-0.216-0.0231.000-0.053-0.085-0.116-0.214-0.212-0.2180.142
depth0.0300.049-0.0531.000-0.2450.010-0.023-0.0250.1030.406
table0.1950.028-0.085-0.2451.0000.1720.2020.1960.1600.290
price0.9630.150-0.1160.0100.1721.0000.9640.9630.9590.093
x0.9970.245-0.214-0.0230.2020.9641.0000.9980.9890.133
y0.9960.245-0.212-0.0250.1960.9630.9981.0000.9880.136
z0.9950.251-0.2180.1030.1600.9590.9890.9881.0000.134
cut0.1130.0360.1420.4060.2900.0930.1330.1360.1341.000

Missing values

2023-01-25T13:27:46.815546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-25T13:27:46.968035image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

caratcutcolorclaritydepthtablepricexyz
00.2321361.555.03263.953.982.43
10.2131259.861.03263.893.842.31
20.2311456.965.03274.054.072.31
30.2935562.458.03344.204.232.63
40.3116363.358.03354.344.352.75
50.2446762.857.03363.943.962.48
60.2445662.357.03363.953.982.47
70.2644261.955.03374.074.112.53
80.2201565.161.03373.873.782.49
90.2344459.461.03384.004.052.39
caratcutcolorclaritydepthtablepricexyz
539300.7131260.555.027565.795.743.49
539310.7132259.862.027565.745.733.43
539320.7041560.559.027575.715.763.47
539330.7041561.259.027575.695.723.49
539340.7230262.759.027575.695.733.58
539350.7220260.857.027575.755.763.50
539360.7210263.155.027575.695.753.61
539370.7040262.860.027575.665.683.56
539380.8634361.058.027576.156.123.74
539390.7520362.255.027575.835.873.64

Duplicate rows

Most frequently occurring

caratcutcolorclaritydepthtablepricexyz# duplicates
830.7923262.357.028985.905.853.665
00.3016463.457.03944.234.262.692
10.3023162.155.08634.324.352.692
20.3023563.055.06754.314.292.712
30.3024262.257.04504.264.292.662
40.3024262.257.04504.274.282.662
50.3030262.258.07094.314.282.672
60.3043563.055.05264.294.312.712
70.3046463.457.05064.264.232.692
80.3110263.556.05714.294.312.732